Methods for generating data insights using ai and natural language processing

Abstract

A method, system, and non-transitory computer readable medium includes receiving a response to a survey from a client device, where the survey is transmitted to the client device and the response is received via a link. The method then generates insight data by analyzing marketing data through a machine learning model. This marketing data is produced using a natural language processor that examines a prompt derived from the survey response. Finally, the method includes providing a graphical user interface on the client device that displays the generated insight data.

What is claimed is: 
     
         1 . A method comprising:
 receiving, by a computing device, a response to a survey from a client device, wherein the survey is transmitted to the client device and the response is received from the client device via a link;   generating, by the computing device, insight data based on marketing data using a machine learning model, wherein the marketing data is generated using a natural language processor by analyzing a prompt based on the response received via the link from the client device; and   providing, by the computing device to the client device, a graphical user interface comprising the insight data.   
     
     
         2 . The method as set forth in  claim 1 , wherein the survey comprises inquiries related to a market, a transaction, or real estate client preferences. 
     
     
         3 . The method as set forth in  claim 1 , further comprises:
 storing, by the computing device, the marketing data and the insight data in a centralized repository;   receiving, by the computing device, a query from the client device relating to the marketing data or the insight data in the centralized repository; and   transmitting, by the computing device, a query response to the client device.   
     
     
         4 . The method as set forth in  claim 3 , further comprises:
 receiving, by the computing device, a login request from the client device;   authenticating, by the computing device, the client device based on login data in the login request;   receiving, by the computing device, the query related to the marketing data or the insight data; and   generating, by the computing device, the query response using the machine learning model by:
 tokenizing the query for key components; 
 matching the key components to a cluster of vectors; and 
 generating the query response based on the cluster of vectors. 
   
     
     
         5 . The method as set forth in  claim 1 , further comprising:
 receiving, by the computing device from the client device, an executive summary request and input relating to the executive summary request;   retrieving, by the computing device, related data to the executive summary request or the input from a centralized repository;   transmitting, by the computing device, a prompt comprising the executive summary request, the input, and the related data to a large language model;   receiving, by the computing device, the executive summary from the large language model; and   transmitting, by the computing device, the executive summary to the client device.   
     
     
         6 . The method as set forth in  claim 1 , further comprising:
 receiving, by the computing device from the client device, a document request;   modifying and providing, by the computing device, the graphical user interface to the client device, wherein the modified graphical user interface comprises an interactive chat configured to request and receive input from the client device for the document request;   transmitting, by the computing device, a prompt comprising the document request and the input to a large language model;   receiving, by the computing device, a document from the large language model, wherein the document meets requirements of the document request and comprises the input from the client device; and   transmitting, by the computing device, the document to the client device.   
     
     
         7 . The method as set forth in  claim 6 , further comprising:
 receiving, by the computing device, edits for the document from the client device;   modifying, by the computing device, the document using the edits from the client device; and   providing, by the computing device, the modified document to the client device.   
     
     
         8 . A marketing computing system comprising:
 one or more processors;   a memory comprising programmed instructions stored thereon, the one or more processors configured to be capable of executing the stored programmed instructions to:
 receive a response to a survey from a client device, wherein the survey is transmitted to the client device and the response is received from the client device via a link; 
 generate insight data based on marketing data using a machine learning model, wherein the marketing data is generated using a natural language processor by analyzing a prompt based on the response received via the link from the client device; and 
 provide, to the client device, a graphical user interface comprising the insight data. 
   
     
     
         9 . The system as set forth in  claim 8 , wherein the survey comprises inquiries related to a market, a transaction, or real estate client preferences. 
     
     
         10 . The system as set forth in  claim 8 , wherein the executable code when executed by the one or more processors further causes the one or more processors to:
 store the marketing data and the insight data in a centralized repository;   receive a query from the client device relating to the marketing data or the insight data in the centralized repository; and   transmit a query response to the client device.   
     
     
         11 . The system as set forth in  claim 10 , wherein the executable code when executed by the one or more processors further causes the one or more processors to:
 receive a login request from the client device;   authenticate the client device based on login data in the login request;   receive the query related to the marketing data or the insight data; and   generate the query response using the machine learning model by:
 tokenizing the query for key components; 
 matching the key components to a cluster of vectors; and 
 generating the query response based on the cluster of vectors. 
   
     
     
         12 . The system as set forth in  claim 8 , wherein the executable code when executed by the one or more processors further causes the one or more processors to:
 receive, from the client device, an executive summary request and input relating to the executive summary request;   retrieve related data to the executive summary request or the input from a centralized repository;   transmit a prompt comprising the executive summary request, the input, and the related data to a large language model;   receive the executive summary from the large language model; and   transmit the executive summary to the client device.   
     
     
         13 . The system as set forth in  claim 8 , wherein the executable code when executed by the one or more processors further causes the one or more processors to:
 receive, from the client device, a document request;   modify and provide the graphical user interface to the client device, wherein the modified graphical user interface comprises an interactive chat configured to request and receive input from the client device for the document request;   transmit a prompt comprising the document request and the input to a large language model;   receive a document from the large language model, wherein the document meets requirements of the document request and comprises the input from the client device; and   transmit the document to the client device.   
     
     
         14 . The system as set forth in  claim 13 , wherein the executable code when executed by the one or more processors further causes the one or more processors to:
 receive edits for the document from the client device;   modify the document using the edits from the client device; and   provide the modified document to the client device.   
     
     
         15 . A non-transitory computer readable medium having stored thereon instructions comprising executable code which when executed by one or more processors, causes the one or more processors to:
 receive a response to a survey from a client device, wherein the survey is transmitted to the client device and the response is received from the client device via a link;   generate insight data based on marketing data using a machine learning model, wherein the marketing data is generated using a natural language processor by analyzing a prompt based on the response received via the link from the client device; and   provide, to the client device, a graphical user interface comprising the insight data.   
     
     
         16 . The medium as set forth in  claim 15 , wherein the survey comprises inquiries related to a market, a transaction, or real estate client preferences. 
     
     
         17 . The medium as set forth in  claim 15 , wherein the executable code when executed by the one or more processors further causes the one or more processors to:
 store the marketing data and the insight data in a centralized repository;   receive a query from the client device relating to the marketing data or the insight data in the centralized repository; and   transmit a query response to the client device.   
     
     
         18 . The medium as set forth in  claim 17 , wherein the executable code when executed by the one or more processors further causes the one or more processors to:
 receive a login request from the client device;   authenticate the client device based on login data in the login request;   receive the query related to the marketing data or the insight data; and   generate the query response using the machine learning model by:
 tokenizing the query for key components; 
 matching the key components to a cluster of vectors; and 
 generating the query response based on the cluster of vectors. 
   
     
     
         19 . The medium as set forth in  claim 15 , wherein the executable code when executed by the one or more processors further causes the one or more processors to:
 receive, from the client device, an executive summary request and input relating to the executive summary request;   retrieve related data to the executive summary request or the input from a centralized repository;   transmit a prompt comprising the executive summary request, the input, and the related data to a large language model;   receive the executive summary from the large language model; and   transmit the executive summary to the client device.   
     
     
         20 . The medium as set forth in  claim 15 , wherein the executable code when executed by the one or more processors further causes the one or more processors to:
 receive, from the client device, a document request;   modify and provide the graphical user interface to the client device, wherein the modified graphical user interface comprises an interactive chat configured to request and receive input from the client device for the document request;   transmit a prompt comprising the document request and the input to a large language model;   receive a document from the large language model, wherein the document meets requirements of the document request and comprises the input from the client device; and   transmit the document to the client device.